Porting pyMC2 Bayesian A/B testing example to pyMC3

I am working to learn pyMC 3 and having some trouble. Since there are limited tutorials for pyMC3 I am working from Bayesian Methods for Hackers. I'm trying to port the pyMC 2 code to pyMC 3 in the Bayesian A/B testing example, with no success. From what I can see the model isn't taking into account the observations at all.

I've had to make a few changes from the example, as pyMC 3 is quite different, so what should look like this:
import pymc as pm

# The parameters are the bounds of the Uniform.
p = pm.Uniform('p', lower=0, upper=1)

# set constants
p_true = 0.05 # remember, this is unknown.
N = 1500

# sample N Bernoulli random variables from Ber(0.05).
# each random variable has a 0.05 chance of being a 1.
# this is the data-generation step
occurrences = pm.rbernoulli(p_true, N)

Apologies for the lengthy post but in my adaptation there have been a number of small changes, e.g. manually generating the observations because pm.rbernoulli no longer exists. I'm also not sure if I should be finding the start prior to running the trace. How should I change my implementation to correctly run?

is wrong as you are just setting a constant value for p (p_true == 0.05). Thus, your random variable p defined above to have a uniform prior is not constrained by the likelihood and your plot shows that you are just sampling from the prior. If you replace p_true with p in your code it should work. Here is the fixed version: